{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "62bc1215",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "\n",
    "file = r'.\\data.xls'\n",
    "data = pd.read_excel(file,'Sheet1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e6c9b22d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "951\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>gy</td>        <th>  R-squared:         </th> <td>   0.006</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   1.748</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 29 Apr 2021</td> <th>  Prob (F-statistic):</th>  <td> 0.156</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>09:10:04</td>     <th>  Log-Likelihood:    </th> <td> -1138.2</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   951</td>      <th>  AIC:               </th> <td>   2284.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   947</td>      <th>  BIC:               </th> <td>   2304.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     3</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>  -96.5909</td> <td>   89.871</td> <td>   -1.075</td> <td> 0.283</td> <td> -272.961</td> <td>   79.779</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>add</th>   <td>   -0.0072</td> <td>    0.004</td> <td>   -1.823</td> <td> 0.069</td> <td>   -0.015</td> <td>    0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>t</th>     <td>   -0.0056</td> <td>    0.007</td> <td>   -0.833</td> <td> 0.405</td> <td>   -0.019</td> <td>    0.008</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>n</th>     <td>    0.0479</td> <td>    0.045</td> <td>    1.077</td> <td> 0.282</td> <td>   -0.039</td> <td>    0.135</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>2054.427</td> <th>  Durbin-Watson:     </th>  <td>   1.876</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>4899997.456</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td>17.998</td>  <th>  Prob(JB):          </th>  <td>    0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td>352.805</td> <th>  Cond. No.          </th>  <td>6.97e+06</td>  \n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 6.97e+06. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                     gy   R-squared:                       0.006\n",
       "Model:                            OLS   Adj. R-squared:                  0.002\n",
       "Method:                 Least Squares   F-statistic:                     1.748\n",
       "Date:                Thu, 29 Apr 2021   Prob (F-statistic):              0.156\n",
       "Time:                        09:10:04   Log-Likelihood:                -1138.2\n",
       "No. Observations:                 951   AIC:                             2284.\n",
       "Df Residuals:                     947   BIC:                             2304.\n",
       "Df Model:                           3                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const        -96.5909     89.871     -1.075      0.283    -272.961      79.779\n",
       "add           -0.0072      0.004     -1.823      0.069      -0.015       0.001\n",
       "t             -0.0056      0.007     -0.833      0.405      -0.019       0.008\n",
       "n              0.0479      0.045      1.077      0.282      -0.039       0.135\n",
       "==============================================================================\n",
       "Omnibus:                     2054.427   Durbin-Watson:                   1.876\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):          4899997.456\n",
       "Skew:                          17.998   Prob(JB):                         0.00\n",
       "Kurtosis:                     352.805   Cond. No.                     6.97e+06\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 6.97e+06. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "data.columns = ['add', 't', 'n','g','gs','y','j','gg','gy','yj', 'name','t1','add1']\n",
    "#可以通过subset参数来删除在gg和add中含有空数据的全部行\n",
    "data1 = data.dropna(subset=[\"gy\", 'add', 't', 'n'])\n",
    "print(len(data1))\n",
    "x = sm.add_constant(data1.iloc[:,:3]) #生成自变量\n",
    "y = data1['gy'] #生成因变量\n",
    "model = sm.OLS(y, x) #生成模型\n",
    "result = model.fit() #模型拟合\n",
    "result.summary() #模型描述"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "eecaf218",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x219d12cfac0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei'] # 中文支持\n",
    "plt.scatter(data1['add1'], data1['gy'], marker='o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9580c78f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x219d14c3640>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data1['n'], data1['gy'], marker='o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "19fcd5ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x219d147eca0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data1['t1'], data1['gy'], marker='o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "586ef2e8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "py8",
   "language": "python",
   "name": "py8"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
